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A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management

Author

Listed:
  • Salman Khalid

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Jinwoo Song

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Muhammad Muzammil Azad

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Muhammad Umar Elahi

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Jaehun Lee

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Soo-Ho Jo

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of Korea)

  • Heung Soo Kim

    (Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of Korea)

Abstract

This review paper addresses the critical need for structural prognostics and health management (SPHM) in aircraft maintenance, highlighting its role in identifying potential structural issues and proactively managing aircraft health. With a comprehensive assessment of various SPHM techniques, the paper contributes by comparing traditional and modern approaches, evaluating their limitations, and showcasing advancements in data-driven and model-based methodologies. It explores the implementation of machine learning and deep learning algorithms, emphasizing their effectiveness in improving prognostic capabilities. Furthermore, it explores model-based approaches, including finite element analysis and damage mechanics, illuminating their potential in the diagnosis and prediction of structural health issues. The impact of digital twin technology in SPHM is also examined, presenting real-life case studies that demonstrate its practical implications and benefits. Overall, this review paper will inform and guide researchers, engineers, and maintenance professionals in developing effective strategies to ensure aircraft safety and structural integrity.

Suggested Citation

  • Salman Khalid & Jinwoo Song & Muhammad Muzammil Azad & Muhammad Umar Elahi & Jaehun Lee & Soo-Ho Jo & Heung Soo Kim, 2023. "A Comprehensive Review of Emerging Trends in Aircraft Structural Prognostics and Health Management," Mathematics, MDPI, vol. 11(18), pages 1-42, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3837-:d:1235098
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    References listed on IDEAS

    as
    1. Salman Khalid & Jinwoo Song & Izaz Raouf & Heung Soo Kim, 2023. "Advances in Fault Detection and Diagnosis for Thermal Power Plants: A Review of Intelligent Techniques," Mathematics, MDPI, vol. 11(8), pages 1-28, April.
    2. Salman Khalid & Jaehun Lee & Heung Soo Kim, 2022. "Series Solution-Based Approach for the Interlaminar Stress Analysis of Smart Composites under Thermo-Electro-Mechanical Loading," Mathematics, MDPI, vol. 10(2), pages 1-15, January.
    3. Hu, Yang & Miao, Xuewen & Si, Yong & Pan, Ershun & Zio, Enrico, 2022. "Prognostics and health management: A review from the perspectives of design, development and decision," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    4. Salman Khalid & Hyunho Hwang & Heung Soo Kim, 2021. "Real-World Data-Driven Machine-Learning-Based Optimal Sensor Selection Approach for Equipment Fault Detection in a Thermal Power Plant," Mathematics, MDPI, vol. 9(21), pages 1-27, November.
    5. Yang, Xuehua & Wu, Lijiao & Zhang, Haixiang, 2023. "A space-time spectral order sinc-collocation method for the fourth-order nonlocal heat model arising in viscoelasticity," Applied Mathematics and Computation, Elsevier, vol. 457(C).
    6. Salman Khalid & Hee-Seong Kim & Heung Soo Kim & Joo-Ho Choi, 2022. "Inspection Interval Optimization for Aircraft Composite Tail Wing Structure Using Numerical-Analysis-Based Approach," Mathematics, MDPI, vol. 10(20), pages 1-18, October.
    7. Manuel Arias Chao & Chetan Kulkarni & Kai Goebel & Olga Fink, 2021. "Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics," Data, MDPI, vol. 6(1), pages 1-14, January.
    8. Sriram, Chellappan & Haghani, Ali, 2003. "An optimization model for aircraft maintenance scheduling and re-assignment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(1), pages 29-48, January.
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